Learning-based Stage Verification System in Manual Assembly Scenarios

• URL: http://arxiv.org/abs/2507.17304v1
• Date: Wed, 23 Jul 2025 08:10:27 GMT
• Title: Learning-based Stage Verification System in Manual Assembly Scenarios
• Authors: Xingjian Zhang, Yutong Duan, Zaishu Chen,
• Abstract summary: This study presents a novel approach to achieve precise monitoring under the limitation of using a minimal number of visual sensors.<n>By integrating state information from identical timestamps, our method detects and confirms the current stage of the assembly process with an average accuracy exceeding 92%.
• Score: 2.517043342442487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the context of Industry 4.0, effective monitoring of multiple targets and states during assembly processes is crucial, particularly when constrained to using only visual sensors. Traditional methods often rely on either multiple sensor types or complex hardware setups to achieve high accuracy in monitoring, which can be cost-prohibitive and difficult to implement in dynamic industrial environments. This study presents a novel approach that leverages multiple machine learning models to achieve precise monitoring under the limitation of using a minimal number of visual sensors. By integrating state information from identical timestamps, our method detects and confirms the current stage of the assembly process with an average accuracy exceeding 92%. Furthermore, our approach surpasses conventional methods by offering enhanced error detection and visuali-zation capabilities, providing real-time, actionable guidance to operators. This not only improves the accuracy and efficiency of assembly monitoring but also re-duces dependency on expensive hardware solutions, making it a more practical choice for modern industrial applications.

Related papers

• Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks [49.84182981950623]
  Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task.<n>It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the ability to address issues such as spectral discrepancies.<n>We introduce an efficient and easily deployable multispectral object detection framework that can seamlessly optimize high-performing single-modality models.
  arXiv  Detail & Related papers  (2024-11-27T12:18:39Z)
• Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images [15.12889076965307]
  YOLOv7 one-stage detector is subjected to a novel meta-learning training framework. This transformation allows the detector to adeptly address FSOD tasks while capitalizing on its inherent advantage of lightweight. To validate the effectiveness of our proposed detector, we conducted performance comparisons with current state-of-the-art detectors.
  arXiv  Detail & Related papers  (2024-04-29T04:56:52Z)
• MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection [34.81779490744863]
  We provide a comprehensive review and evaluation of twelve state-of-the-art anomaly detection methods. We propose a novel metric called salience to better understand the characteristics of different anomaly detectors. We report the benchmark results in terms of accuracy, salience, efficiency, and delay, which are of practical importance for industrial deployment.
  arXiv  Detail & Related papers  (2024-01-10T06:50:25Z)
• Cal-DETR: Calibrated Detection Transformer [67.75361289429013]
  We propose a mechanism for calibrated detection transformers (Cal-DETR), particularly for Deformable-DETR, UP-DETR and DINO. We develop an uncertainty-guided logit modulation mechanism that leverages the uncertainty to modulate the class logits. Results corroborate the effectiveness of Cal-DETR against the competing train-time methods in calibrating both in-domain and out-domain detections.
  arXiv  Detail & Related papers  (2023-11-06T22:13:10Z)
• Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis [0.21847754147782888]
  We present a Denoising Multi-Modal Autoencoder with a unique training strategy based on contrastive learning paradigm. The presented approach achieves excellent performance in fusing multiple modalities (or views) of data into an enriched common representation. The presented methodology enables multi-modal fault diagnosis systems to perform more robustly in case of sensor failure occurrence.
  arXiv  Detail & Related papers  (2023-11-04T00:04:09Z)
• Agile gesture recognition for capacitive sensing devices: adapting on-the-job [55.40855017016652]
  We demonstrate a hand gesture recognition system that uses signals from capacitive sensors embedded into the etee hand controller. The controller generates real-time signals from each of the wearer five fingers. We use a machine learning technique to analyse the time series signals and identify three features that can represent 5 fingers within 500 ms.
  arXiv  Detail & Related papers  (2023-05-12T17:24:02Z)
• Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection [54.92703325989853]
  We propose a two-stage Activation-to-Saliency (A2S) framework that effectively generates high-quality saliency cues. No human annotations are involved in our framework during the whole training process. Our framework reports significant performance compared with existing USOD methods.
  arXiv  Detail & Related papers  (2021-12-07T11:54:06Z)
• DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series [88.12892448747291]
  We introduce the DeTAVIZ interface, which is a web browser based visualization tool for quick exploration and assessment of feasibility of DL based anomaly detection in a given problem. DeTAVIZ allows the user to easily and quickly iterate through multiple post processing options and compare different models, and allows for manual optimisation towards a chosen metric.
  arXiv  Detail & Related papers  (2021-09-21T10:38:26Z)
• Machine Learning based Indicators to Enhance Process Monitoring by Pattern Recognition [0.4893345190925177]
  We propose a novel framework for machine learning based indicators combining pattern type and intensity. In a case-study from semiconductor industry, our framework goes beyond conventional process control and achieves high quality experimental results.
  arXiv  Detail & Related papers  (2021-03-24T10:13:20Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)
• Collaborative Inference for Efficient Remote Monitoring [34.27630312942825]
  A naive approach to resolve this on the model level is to use simpler architectures. We propose an alternative solution by decomposing the predictive model as the sum of a simple function which serves as a local monitoring tool. A sign requirement is imposed on the latter to ensure that the local monitoring function is safe.
  arXiv  Detail & Related papers  (2020-02-12T01:57:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.